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Value Stream Mapping (VSM) is a Lean method for waste reduction, crucial across industries. In the era of the fourth industrial revolution, Small and Medium-sized Enterprises (SMEs) are adopting smart, sustainable practices. The focus has shifted from traditional VSM to Smart Sustainable VSM (SS-VSM). This study introduces an SS-VSM framework, integrating lean, sustainable, and smart techniques. A fuzzy-based Preference Selection Index (FPSI) method selects the optimal SS-VSM strategy, considering environmental, economic, and social factors. Sensitivity analysis tests the approach's robustness. The results offer a conceptual framework for implementing and managing VSM, aiding SMEs in enhancing performance through SS-VSM practices.

The social and economic development of the islands is generally dependent on the interconnection level with the mainland obtained through maritime transport services. When connecting many islands, the route planning is essential and typically a variety of constraints must be considered. Various optimization methods have been established to improve cost-efficiency but today environmental concerns, like the reduction of CO2 emissions, have become mandatory. This paper proposes a vessel scheduling optimization model able to simultaneously consider compulsory and optional tasks and vehicle-dependent profits. The algorithm was applied to seven islands of the Tyrrhenian Sea located in front of Sicily, named “Aeolian Islands”. Considering the regional requirements in terms of minimum number of routes and maximum fare prices for each season, this research compared the optimal vessels option obtained maximizing the profit with the one obtained minimizing the emissions. In particular, we have conducted three different analyses, in the first one we have considered only the mandatory routes while the second one was carried out identifying a series of potentially attractive additional activities based on historical demand data provided by the company that currently manages the service. Finally, the third analysis addresses a scenario where older fuel-powered vessels were replaced with hybrid electric ones.

Industry 4 [...]

Recent developments in manufacturing processes and automation have led to the new industrial revolution termed “Industry 4.0”. Industry 4.0 can be considered as a broad domain which includes: data management, manufacturing competitiveness, production processes and efficiency. The term Industry 4.0 includes a variety of key enabling technologies i.e., cyber physical systems, Internet of Things, artificial intelligence, big data analytics and digital twins which can be considered as the major contributors to automated and digital manufacturing environments. Sustainability can be considered as the core of business strategy which is highlighted in the United Nations (UN) Sustainability 2030 agenda and includes smart manufacturing, energy efficient buildings and low-impact industrialization. Industry 4.0 technologies help to achieve sustainability in business practices. However, very limited studies reported about the extensive reviews on these two research areas. This study uses a systematic literature review approach to find out the current research progress and future research potential of Industry 4.0 technologies to achieve manufacturing sustainability. The role and impact of different Industry 4.0 technologies for manufacturing sustainability is discussed in detail. The findings of this study provide new research scopes and future research directions in different research areas of Industry 4.0 which will be valuable for industry and academia in order to achieve manufacturing sustainability with Industry 4.0 technologies.

In a sustainable management of logistics, transportation plays a crucial role. Traditionally, the main purpose was to solve the Vehicle Routing Problem minimizing the cost associated with the travelled distances. Nowadays, the economic profit cannot be the only driver for achieving sustainability and environmental issues have to be also considered. In this paper, to satisfy the intricate limits involved in real vehicle routing problem, the study has been structured considering different types of vehicles in terms of maximum capacity, velocity and emissions, asymmetric paths, vehicle-client constraints and delivery time windows. The firefly algorithm has been implemented to solve the vehicle routing problem and the TOPSIS technique has been applied to integrate economic and environmental factors. Finally, to prove the effectiveness of the proposed approach, a numerical example has been proposed using data provided by a logistic company located in Sicily.